A synthesis of dryland restoration techniques.

Purpose

To quantitatively examine the efficacy of vegetation restoration in drylands globally.

Questions

1. What is the global extent of research that directly examined restoration of drylands?
   
2. What were the common measures?
   
3. Is the restoration of vegetation a common and primary focus?
   
4. How frequently does the restoration measure outcomes beyond the focal species?
   
5. What were the primary restoration goals as reported by primary authors?
   
6. How much variation was there in the techniques tested and how long were experiments monitored and tested?
   
7. How relatively effective were the techniques?

Step 1. Search

Study-level viz to document patterns in exclusions primarily and the relatie frequenices, at the study level, of major categories of evidence.

#study data####
library(tidyverse)
studies <- read_csv("data/studies.csv")
studies

## # A tibble: 278 x 18
##       ID title technique data  region exclude rationale observations
##    <dbl> <chr> <chr>     <chr> <chr>  <chr>   <chr>     <chr>       
##  1   152 Shor… seeding,… expe… Africa no      <NA>      <NA>        
##  2   180 Rest… chemical… App.… Africa no      <NA>      <NA>        
##  3   229 Infl… soil see… fiel… Africa no      <NA>      <NA>        
##  4   230 Acti… planting  fiel… Africa no      <NA>      <NA>        
##  5   255 The … grazing … fiel… Africa no      <NA>      <NA>        
##  6   262 Reve… seeding,… eper… Africa no      <NA>      <NA>        
##  7   263 The … phytogen… fiel… Africa no      <NA>      <NA>        
##  8   264 Eval… seeding,… fiel… Africa no      <NA>      <NA>        
##  9   271 Patc… natural … fiel… Africa no      <NA>      <NA>        
## 10     4 Fact… natural … App.… Africa no      <NA>      <NA>        
## # ... with 268 more rows, and 10 more variables: disturbance <chr>,
## #   system <chr>, goal <chr>, intervention <chr>, paradigm <chr>,
## #   grazing <chr>, hypothesis <chr>, soil <chr>, benchmark <chr>,
## #   notes <chr>

#quick look at rationale needed
exclusions <- studies %>%
  filter(exclude == "yes")

#quick look at studies with paradigms
evidence <- studies %>%
  filter(exclude == "no")

#library(skimr)
#skim(evidence)

#study-level viz#####
#exclusions
ggplot(exclusions, aes(rationale, fill = region)) +
  geom_bar() +
  coord_flip() +
  labs(x = "rational for exclusion", y = "frequency") +
  scale_fill_brewer(palette = "Paired")

ggplot(evidence, aes(disturbance, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

ggplot(evidence, aes(region, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

ggplot(evidence, aes(data, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

ggplot(evidence, aes(system, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(y = "frequency")

ggplot(evidence, aes(goal, fill = paradigm)) +
  geom_bar(na.rm = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Paired") +
  labs(x = "outcome", y = "frequency")

#step 1 models####
#paradigm
derived.evidence <- evidence %>%
  group_by(technique, data, region, disturbance, goal, paradigm) %>% summarise(n = n())

#active-passive split
m <- glm(n~paradigm, family = poisson, derived.evidence)
anova(m, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##          Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                       167     9.9147         
## paradigm  1 0.045115       166     9.8696   0.8318

#region
m1 <- glm(n~paradigm*region, family = poisson, derived.evidence)
#m1
#summary(m1)
anova(m1, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##                 Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                              167     9.9147         
## paradigm         1 0.045115       166     9.8696   0.8318
## region           6 0.301367       160     9.5682   0.9995
## paradigm:region  6 0.213627       154     9.3546   0.9998

#outcome
m2 <- glm(n~paradigm*goal, family = poisson, derived.evidence)
#m1
#summary(m1)
anova(m2, test="Chisq")

## Analysis of Deviance Table
## 
## Model: poisson, link: log
## 
## Response: n
## 
## Terms added sequentially (first to last)
## 
## 
##               Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL                            167     9.9147         
## paradigm       1 0.045115       166     9.8696   0.8318
## goal           6 0.240941       160     9.6287   0.9997
## paradigm:goal  4 0.301480       156     9.3272   0.9897

#even split between active and passive evidence by all key categories

Step 2. Sort

A summary of sort process using PRISMA.

library(PRISMAstatement)
prisma(found = 1504,
       found_other = 5,
       no_dupes = 1039, 
       screened = 1039, 
       screen_exclusions = 861, 
       full_text = 178,
       full_text_exclusions = 100, 
       qualitative = 78, 
       quantitative = 78,
       width = 800, height = 800)

Step 3. Synthesize

Check data and calculate necessary measures.

data <- read_csv("data/data.csv")
data <- data %>%
  mutate(lrr = log(mean.t/mean.c), rii = ((mean.t-mean.c)/(mean.t + mean.c)), var.es = ((sd.t^2/n.t*mean.t^2) + (sd.c^2/n.c*mean.c^2)))
#data

#consider adding some other effect size measures and/or study-level data too

Step 4. Summarize

Explore summary level data of all data. Explore aggregation levels that support the most reasonable data structure and minimize non-independence issues.

#evidence map####
require(maps)
world<-map_data("world")
map<-ggplot() + geom_polygon(data=world, fill="gray50", aes(x=long, y=lat, group=group))
map + geom_point(data=data, aes(x=long, y=lat)) +
  labs(x = "longitude", y = "latitude") #render a literal map, i.e. evidence map, of where we study the niche in deserts globally

#add in levels and color code points on map####
map + geom_point(data=data, aes(x=long, y=lat, color = paradigm)) + 
  scale_color_brewer(palette = "Paired") +
  labs(x = "longitude", y = "latitude", color = "")

#aggregation####
se <- function(x){
  sd(x)/sqrt(length(x))
}

data.simple <- data %>%
  group_by(study.ID, paradigm, technique, measure.success) %>%
  summarise(n = n(), mean.lrr = mean(lrr), mean.rii = mean(rii), mean.var = mean(var.es))

main.data <- data %>%
  group_by(study.ID, paradigm, intervention, outcome) %>%
  summarise(n = n(), mean.lrr = mean(lrr), mean.rii = mean(rii), mean.var = mean(var.es))


#EDA data####
simple.data <- data %>% group_by(study.ID, paradigm, technique, measure.success) %>% summarise(mean.rii = mean(rii), error = se(rii))
simple.data <- na.omit(simple.data)

parad.data <- data %>% group_by(study.ID, paradigm) %>% summarise(mean.rii = mean(rii), error = se(rii))
parad.data <- na.omit(parad.data)

tech.data <- data %>% group_by(study.ID, technique) %>% summarise(mean.rii = mean(rii), error = se(rii))
tech.data <- na.omit(tech.data)

success.data <- data %>% group_by(study.ID, measure.success) %>% summarise(mean.rii = mean(rii), error = se(rii))
success.data <- na.omit(success.data)

#active
active <- data %>%
  filter(paradigm == "active")

#viz for aggregation####
ggplot(na.omit(data.simple), aes(technique, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired")

ggplot(na.omit(data.simple), aes(measure.success, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired")

#better
ggplot(main.data, aes(intervention, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired") +
  labs(fill = "")

ggplot(main.data, aes(outcome, n, fill = paradigm)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_brewer(palette = "Paired") +
  labs(fill = "")

Step 5a. EDA

Exploratory data analyses to understand data and QA/QC.

#library(plotrix) #for quick s.e. calculations sometimes needed for data tidy step
library(meta) #nice package for most meta-statistics

#assign model (typically a nice meta. function from one of several packages such as meta, metafor, or netmeta)

#need to write a quick function here to iterate and/or up split-map from broom

#EDA####

#rii####
#paradigm
m <- metagen(mean.rii, error, studlab = study.ID, byvar = paradigm, data = simple.data) #fit generic meta-analysis to an object
m

##                         95%-CI %W(fixed) %W(random) paradigm
## 1    0.1004 [-0.0416;  0.2424]       0.5        3.6  passive
## 3   -0.1868 [-0.2919; -0.0817]       0.9        3.9   active
## 6    0.1973 [-0.1756;  0.5702]       0.1        2.0   active
## 9    0.1901 [ 0.1523;  0.2280]       7.3        4.2  passive
## 13  -0.0083 [-0.0913;  0.0747]       1.5        4.0   active
## 20  -0.2808 [-0.3514; -0.2102]       2.1        4.1   active
## 29  -0.2971 [-0.3868; -0.2073]       1.3        4.0   active
## 30  -0.0326 [-0.1910;  0.1257]       0.4        3.5   active
## 31   0.3318 [ 0.1006;  0.5630]       0.2        2.9  passive
## 32   0.3908 [ 0.3293;  0.4522]       2.8        4.1   active
## 51   0.1280 [-0.0646;  0.3205]       0.3        3.2   active
## 66   0.0762 [ 0.0222;  0.1302]       3.6        4.1  passive
## 68  -0.1772 [-0.2845; -0.0699]       0.9        3.9   active
## 69   0.5020 [ 0.3552;  0.6489]       0.5        3.6  passive
## 77   0.0613 [-0.8629;  0.9854]       0.0        0.5   active
## 87  -0.0138 [-0.0406;  0.0131]      14.4        4.2   active
## 88  -0.1297 [-0.1597; -0.0997]      11.5        4.2   active
## 95  -0.2280 [-0.3094; -0.1466]       1.6        4.0   active
## 104  0.3069 [ 0.2619;  0.3519]       5.1        4.2   active
## 109  0.4675 [ 0.2613;  0.6737]       0.2        3.1   active
## 111  0.0748 [ 0.0406;  0.1091]       8.9        4.2   active
## 121 -0.0229 [-0.0432; -0.0026]      25.2        4.2   active
## 135  0.0859 [ 0.0317;  0.1401]       3.5        4.1  passive
## 147 -0.2642 [-0.4402; -0.0882]       0.3        3.4   active
## 210 -0.3286 [-0.4648; -0.1924]       0.6        3.7   active
## 239  0.2968 [-0.1971;  0.7907]       0.0        1.4  passive
## 247 -0.3379 [-0.3809; -0.2950]       5.6        4.2  passive
## 256  0.0430 [-0.0941;  0.1801]       0.6        3.7  passive
## 
## Number of studies combined: k = 28
## 
##                                         95%-CI     z p-value
## Fixed effect model   -0.0030 [-0.0132; 0.0072] -0.58  0.5589
## Random effects model  0.0168 [-0.0547; 0.0882]  0.46  0.6459
## 
## Quantifying heterogeneity:
## tau^2 = 0.0315; H = 6.23 [5.67; 6.84]; I^2 = 97.4% [96.9%; 97.9%]
## 
## Test of heterogeneity:
##        Q d.f.  p-value
##  1047.16   27 < 0.0001
## 
## Results for subgroups (fixed effect model):
##                      k                    95%-CI      Q  tau^2   I^2
## paradigm = passive   9  0.0205 [-0.0014; 0.0423] 405.03 0.0643 98.0%
## paradigm = active   19 -0.0096 [-0.0211; 0.0019] 636.43 0.0262 97.2%
## 
## Test for subgroup differences (fixed effect model):
##                      Q d.f.  p-value
## Between groups    5.70    1   0.0170
## Within groups  1041.46   26 < 0.0001
## 
## Results for subgroups (random effects model):
##                      k                    95%-CI      Q  tau^2   I^2
## paradigm = passive   9  0.1278 [-0.0472; 0.3028] 405.03 0.0643 98.0%
## paradigm = active   19 -0.0315 [-0.1112; 0.0481] 636.43 0.0262 97.2%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   2.64    1  0.1043
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2

#no difference in random effects model by paradigm but fixed yes. Hetereogeneity is significantly different so a. fixed not representative and b. need a better model
#forest(m,  xlim="symmetric", plotwidth=unit(1, "cm"))
forest(m, sortvar = paradigm)

radial(m)

#metabias(m, method = "linreg")

#technique
m <- metagen(mean.rii, error, studlab = study.ID, byvar = technique, data = tech.data) #fit generic meta-analysis to an object
m

##                         95%-CI %W(fixed) %W(random)
## 1    0.1004 [-0.0416;  0.2424]       0.5        3.6
## 3   -0.1868 [-0.2919; -0.0817]       0.9        3.9
## 6    0.1973 [-0.1756;  0.5702]       0.1        2.0
## 9    0.1901 [ 0.1523;  0.2280]       7.3        4.2
## 13  -0.0083 [-0.0913;  0.0747]       1.5        4.0
## 20  -0.2808 [-0.3514; -0.2102]       2.1        4.1
## 29  -0.2971 [-0.3868; -0.2073]       1.3        4.0
## 30  -0.0326 [-0.1910;  0.1257]       0.4        3.5
## 31   0.3318 [ 0.1006;  0.5630]       0.2        2.9
## 32   0.3908 [ 0.3293;  0.4522]       2.8        4.1
## 51   0.1280 [-0.0646;  0.3205]       0.3        3.2
## 66   0.0762 [ 0.0222;  0.1302]       3.6        4.1
## 68  -0.1772 [-0.2845; -0.0699]       0.9        3.9
## 69   0.5020 [ 0.3552;  0.6489]       0.5        3.6
## 77   0.0613 [-0.8629;  0.9854]       0.0        0.5
## 87  -0.0138 [-0.0406;  0.0131]      14.4        4.2
## 88  -0.1297 [-0.1597; -0.0997]      11.5        4.2
## 95  -0.2280 [-0.3094; -0.1466]       1.6        4.0
## 104  0.3069 [ 0.2619;  0.3519]       5.1        4.2
## 109  0.4675 [ 0.2613;  0.6737]       0.2        3.1
## 111  0.0748 [ 0.0406;  0.1091]       8.9        4.2
## 121 -0.0229 [-0.0432; -0.0026]      25.2        4.2
## 135  0.0859 [ 0.0317;  0.1401]       3.5        4.1
## 147 -0.2642 [-0.4402; -0.0882]       0.3        3.4
## 210 -0.3286 [-0.4648; -0.1924]       0.6        3.7
## 239  0.2968 [-0.1971;  0.7907]       0.0        1.4
## 247 -0.3379 [-0.3809; -0.2950]       5.6        4.2
## 256  0.0430 [-0.0941;  0.1801]       0.6        3.7
##                                 technique
## 1                            litter crust
## 3                 seeding, soil amendment
## 6   seeding, herbicides, activated carbon
## 9                         fertile islands
## 13                               planting
## 20                               planting
## 29            planting, grazing exclusion
## 30                              herbicide
## 31             seed arrival and retention
## 32                               planting
## 51            planting, grazing exclusion
## 66            seeding, shrub facilitation
## 68                               planting
## 69                      grazing exclusion
## 77                        mowing, grazing
## 87  fertilization, biostimulants, seeding
## 88                                seeding
## 95                      seeding, planting
## 104               mycorrhizal inoculation
## 109            seeding, mulching, weeding
## 111                              planting
## 121                              planting
## 135                      natural recovery
## 147                               seeding
## 210                      carbon amendment
## 239                      natural recovery
## 247                      natural recovery
## 256                     mycorrhizal fungi
## 
## Number of studies combined: k = 28
## 
##                                         95%-CI     z p-value
## Fixed effect model   -0.0030 [-0.0132; 0.0072] -0.58  0.5589
## Random effects model  0.0168 [-0.0547; 0.0882]  0.46  0.6459
## 
## Quantifying heterogeneity:
## tau^2 = 0.0315; H = 6.23 [5.67; 6.84]; I^2 = 97.4% [96.9%; 97.9%]
## 
## Test of heterogeneity:
##        Q d.f.  p-value
##  1047.16   27 < 0.0001
## 
## Results for subgroups (fixed effect model):
##                                                   k        
## technique = litter crust                          1  0.1004
## technique = seeding, soil amendment               1 -0.1868
## technique = seeding, herbicides, activated  ...   1  0.1973
## technique = fertile islands                       1  0.1901
## technique = planting                              6  0.0098
## technique = planting, grazing exclusion           2 -0.2213
## technique = herbicide                             1 -0.0326
## technique = seed arrival and retention            1  0.3318
## technique = seeding, shrub facilitation           1  0.0762
## technique = grazing exclusion                     1  0.5020
## technique = mowing, grazing                       1  0.0613
## technique = fertilization, biostimulants, s ...   1 -0.0138
## technique = seeding                               2 -0.1335
## technique = seeding, planting                     1 -0.2280
## technique = mycorrhizal inoculation               1  0.3069
## technique = seeding, mulching, weeding            1  0.4675
## technique = natural recovery                      3 -0.1721
## technique = carbon amendment                      1 -0.3286
## technique = mycorrhizal fungi                     1  0.0430
##                                                             95%-CI      Q
## technique = litter crust                        [-0.0416;  0.2424]   0.00
## technique = seeding, soil amendment             [-0.2919; -0.0817]   0.00
## technique = seeding, herbicides, activated  ... [-0.1756;  0.5702]   0.00
## technique = fertile islands                     [ 0.1523;  0.2280]   0.00
## technique = planting                            [-0.0061;  0.0256] 248.37
## technique = planting, grazing exclusion         [-0.3026; -0.1399]  15.38
## technique = herbicide                           [-0.1910;  0.1257]   0.00
## technique = seed arrival and retention          [ 0.1006;  0.5630]   0.00
## technique = seeding, shrub facilitation         [ 0.0222;  0.1302]   0.00
## technique = grazing exclusion                   [ 0.3552;  0.6489]   0.00
## technique = mowing, grazing                     [-0.8629;  0.9854]   0.00
## technique = fertilization, biostimulants, s ... [-0.0406;  0.0131]   0.00
## technique = seeding                             [-0.1631; -0.1039]   2.18
## technique = seeding, planting                   [-0.3094; -0.1466]   0.00
## technique = mycorrhizal inoculation             [ 0.2619;  0.3519]   0.00
## technique = seeding, mulching, weeding          [ 0.2613;  0.6737]   0.00
## technique = natural recovery                    [-0.2057; -0.1385] 147.89
## technique = carbon amendment                    [-0.4648; -0.1924]   0.00
## technique = mycorrhizal fungi                   [-0.0941;  0.1801]   0.00
##                                                  tau^2   I^2
## technique = litter crust                            --    --
## technique = seeding, soil amendment                 --    --
## technique = seeding, herbicides, activated  ...     --    --
## technique = fertile islands                         --    --
## technique = planting                            0.0278 98.0%
## technique = planting, grazing exclusion         0.0845 93.5%
## technique = herbicide                               --    --
## technique = seed arrival and retention              --    --
## technique = seeding, shrub facilitation             --    --
## technique = grazing exclusion                       --    --
## technique = mowing, grazing                         --    --
## technique = fertilization, biostimulants, s ...     --    --
## technique = seeding                             0.0049 54.1%
## technique = seeding, planting                       --    --
## technique = mycorrhizal inoculation                 --    --
## technique = seeding, mulching, weeding              --    --
## technique = natural recovery                    0.0894 98.6%
## technique = carbon amendment                        --    --
## technique = mycorrhizal fungi                       --    --
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 633.34   18 < 0.0001
## Within groups  413.82    9 < 0.0001
## 
## Results for subgroups (random effects model):
##                                                   k        
## technique = litter crust                          1  0.1004
## technique = seeding, soil amendment               1 -0.1868
## technique = seeding, herbicides, activated  ...   1  0.1973
## technique = fertile islands                       1  0.1901
## technique = planting                              6 -0.0013
## technique = planting, grazing exclusion           2 -0.0934
## technique = herbicide                             1 -0.0326
## technique = seed arrival and retention            1  0.3318
## technique = seeding, shrub facilitation           1  0.0762
## technique = grazing exclusion                     1  0.5020
## technique = mowing, grazing                       1  0.0613
## technique = fertilization, biostimulants, s ...   1 -0.0138
## technique = seeding                               2 -0.1678
## technique = seeding, planting                     1 -0.2280
## technique = mycorrhizal inoculation               1  0.3069
## technique = seeding, mulching, weeding            1  0.4675
## technique = natural recovery                      3 -0.0301
## technique = carbon amendment                      1 -0.3286
## technique = mycorrhizal fungi                     1  0.0430
##                                                             95%-CI      Q
## technique = litter crust                        [-0.0416;  0.2424]   0.00
## technique = seeding, soil amendment             [-0.2919; -0.0817]   0.00
## technique = seeding, herbicides, activated  ... [-0.1756;  0.5702]   0.00
## technique = fertile islands                     [ 0.1523;  0.2280]   0.00
## technique = planting                            [-0.1376;  0.1350] 248.37
## technique = planting, grazing exclusion         [-0.5096;  0.3227]  15.38
## technique = herbicide                           [-0.1910;  0.1257]   0.00
## technique = seed arrival and retention          [ 0.1006;  0.5630]   0.00
## technique = seeding, shrub facilitation         [ 0.0222;  0.1302]   0.00
## technique = grazing exclusion                   [ 0.3552;  0.6489]   0.00
## technique = mowing, grazing                     [-0.8629;  0.9854]   0.00
## technique = fertilization, biostimulants, s ... [-0.0406;  0.0131]   0.00
## technique = seeding                             [-0.2867; -0.0490]   2.18
## technique = seeding, planting                   [-0.3094; -0.1466]   0.00
## technique = mycorrhizal inoculation             [ 0.2619;  0.3519]   0.00
## technique = seeding, mulching, weeding          [ 0.2613;  0.6737]   0.00
## technique = natural recovery                    [-0.3956;  0.3354] 147.89
## technique = carbon amendment                    [-0.4648; -0.1924]   0.00
## technique = mycorrhizal fungi                   [-0.0941;  0.1801]   0.00
##                                                  tau^2   I^2
## technique = litter crust                            --    --
## technique = seeding, soil amendment                 --    --
## technique = seeding, herbicides, activated  ...     --    --
## technique = fertile islands                         --    --
## technique = planting                            0.0278 98.0%
## technique = planting, grazing exclusion         0.0845 93.5%
## technique = herbicide                               --    --
## technique = seed arrival and retention              --    --
## technique = seeding, shrub facilitation             --    --
## technique = grazing exclusion                       --    --
## technique = mowing, grazing                         --    --
## technique = fertilization, biostimulants, s ...     --    --
## technique = seeding                             0.0049 54.1%
## technique = seeding, planting                       --    --
## technique = mycorrhizal inoculation                 --    --
## technique = seeding, mulching, weeding              --    --
## technique = natural recovery                    0.0894 98.6%
## technique = carbon amendment                        --    --
## technique = mycorrhizal fungi                       --    --
## 
## Test for subgroup differences (random effects model):
##                       Q d.f.  p-value
## Between groups   361.83   18 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2

forest(m, sortvar = technique)

radial(m)

#metabias(m, method = "linreg")

#measure
m <- metagen(mean.rii, error, studlab = study.ID, byvar = measure.success, data = success.data) #fit generic meta-analysis to an object
m

##                         95%-CI %W(fixed) %W(random)
## 1    0.1004 [-0.0416;  0.2424]       0.5        3.6
## 3   -0.1868 [-0.2919; -0.0817]       0.9        3.9
## 6    0.1973 [-0.1756;  0.5702]       0.1        2.0
## 9    0.1901 [ 0.1523;  0.2280]       7.3        4.2
## 13  -0.0083 [-0.0913;  0.0747]       1.5        4.0
## 20  -0.2808 [-0.3514; -0.2102]       2.1        4.1
## 29  -0.2971 [-0.3868; -0.2073]       1.3        4.0
## 30  -0.0326 [-0.1910;  0.1257]       0.4        3.5
## 31   0.3318 [ 0.1006;  0.5630]       0.2        2.9
## 32   0.3908 [ 0.3293;  0.4522]       2.8        4.1
## 51   0.1280 [-0.0646;  0.3205]       0.3        3.2
## 66   0.0762 [ 0.0222;  0.1302]       3.6        4.1
## 68  -0.1772 [-0.2845; -0.0699]       0.9        3.9
## 69   0.5020 [ 0.3552;  0.6489]       0.5        3.6
## 77   0.0613 [-0.8629;  0.9854]       0.0        0.5
## 87  -0.0138 [-0.0406;  0.0131]      14.4        4.2
## 88  -0.1297 [-0.1597; -0.0997]      11.5        4.2
## 95  -0.2280 [-0.3094; -0.1466]       1.6        4.0
## 104  0.3069 [ 0.2619;  0.3519]       5.1        4.2
## 109  0.4675 [ 0.2613;  0.6737]       0.2        3.1
## 111  0.0748 [ 0.0406;  0.1091]       8.9        4.2
## 121 -0.0229 [-0.0432; -0.0026]      25.2        4.2
## 135  0.0859 [ 0.0317;  0.1401]       3.5        4.1
## 147 -0.2642 [-0.4402; -0.0882]       0.3        3.4
## 210 -0.3286 [-0.4648; -0.1924]       0.6        3.7
## 239  0.2968 [-0.1971;  0.7907]       0.0        1.4
## 247 -0.3379 [-0.3809; -0.2950]       5.6        4.2
## 256  0.0430 [-0.0941;  0.1801]       0.6        3.7
##                                             measure.success
## 1                                    seedling establishment
## 3                             Triodia wiseana establishment
## 6                            Agropyron desertorum seedlings
## 9                  heterogeneous patterns of soil nutrients
## 13                          natural undisturbed communities
## 20                                            desert steppe
## 29                            grassland vegetation and soil
## 30                                         lizard community
## 31                        seed availability for restoration
## 32  improved soil physicochemical and biological properties
## 51                              diverse arthropod community
## 66                                    grassland restoration
## 68                                    grassland restoration
## 69                                  vegetation regeneration
## 77                                       Narduus grasslands
## 87                          decreasing soil P-concentration
## 88       soil carbon and nitrogen of the original grassland
## 95                               remnant prairie vegetation
## 104                                       native vegetation
## 109                             native shrubs establishment
## 111                                            revegetation
## 121                                            revegetation
## 135                                       native vegetation
## 147                                native perennial grasses
## 210                                  endemic sand grassland
## 239                                native cerrado community
## 247                       soil properties of native forests
## 256                                               not clear
## 
## Number of studies combined: k = 28
## 
##                                         95%-CI     z p-value
## Fixed effect model   -0.0030 [-0.0132; 0.0072] -0.58  0.5589
## Random effects model  0.0168 [-0.0547; 0.0882]  0.46  0.6459
## 
## Quantifying heterogeneity:
## tau^2 = 0.0315; H = 6.23 [5.67; 6.84]; I^2 = 97.4% [96.9%; 97.9%]
## 
## Test of heterogeneity:
##        Q d.f.  p-value
##  1047.16   27 < 0.0001
## 
## Results for subgroups (fixed effect model):
##                                                         k        
## measure.success = seedling establishment                1  0.1004
## measure.success = Triodia wiseana establishment         1 -0.1868
## measure.success = Agropyron desertorum seedlings        1  0.1973
## measure.success = heterogeneous patterns of soil  ...   1  0.1901
## measure.success = natural undisturbed communities       1 -0.0083
## measure.success = desert steppe                         1 -0.2808
## measure.success = grassland vegetation and soil         1 -0.2971
## measure.success = lizard community                      1 -0.0326
## measure.success = seed availability for restoration     1  0.3318
## measure.success = improved soil physicochemical a ...   1  0.3908
## measure.success = diverse arthropod community           1  0.1280
## measure.success = grassland restoration                 2  0.0250
## measure.success = vegetation regeneration               1  0.5020
## measure.success = Narduus grasslands                    1  0.0613
## measure.success = decreasing soil P-concentration       1 -0.0138
## measure.success = soil carbon and nitrogen of the ...   1 -0.1297
## measure.success = remnant prairie vegetation            1 -0.2280
## measure.success = native vegetation                     2  0.2166
## measure.success = native shrubs establishment           1  0.4675
## measure.success = revegetation                          2  0.0025
## measure.success = native perennial grasses              1 -0.2642
## measure.success = endemic sand grassland                1 -0.3286
## measure.success = native cerrado community              1  0.2968
## measure.success = soil properties of native forests     1 -0.3379
## measure.success = not clear                             1  0.0430
##                                                                   95%-CI
## measure.success = seedling establishment              [-0.0416;  0.2424]
## measure.success = Triodia wiseana establishment       [-0.2919; -0.0817]
## measure.success = Agropyron desertorum seedlings      [-0.1756;  0.5702]
## measure.success = heterogeneous patterns of soil  ... [ 0.1523;  0.2280]
## measure.success = natural undisturbed communities     [-0.0913;  0.0747]
## measure.success = desert steppe                       [-0.3514; -0.2102]
## measure.success = grassland vegetation and soil       [-0.3868; -0.2073]
## measure.success = lizard community                    [-0.1910;  0.1257]
## measure.success = seed availability for restoration   [ 0.1006;  0.5630]
## measure.success = improved soil physicochemical a ... [ 0.3293;  0.4522]
## measure.success = diverse arthropod community         [-0.0646;  0.3205]
## measure.success = grassland restoration               [-0.0232;  0.0733]
## measure.success = vegetation regeneration             [ 0.3552;  0.6489]
## measure.success = Narduus grasslands                  [-0.8629;  0.9854]
## measure.success = decreasing soil P-concentration     [-0.0406;  0.0131]
## measure.success = soil carbon and nitrogen of the ... [-0.1597; -0.0997]
## measure.success = remnant prairie vegetation          [-0.3094; -0.1466]
## measure.success = native vegetation                   [ 0.1820;  0.2512]
## measure.success = native shrubs establishment         [ 0.2613;  0.6737]
## measure.success = revegetation                        [-0.0149;  0.0200]
## measure.success = native perennial grasses            [-0.4402; -0.0882]
## measure.success = endemic sand grassland              [-0.4648; -0.1924]
## measure.success = native cerrado community            [-0.1971;  0.7907]
## measure.success = soil properties of native forests   [-0.3809; -0.2950]
## measure.success = not clear                           [-0.0941;  0.1801]
##                                                           Q  tau^2   I^2
## measure.success = seedling establishment               0.00     --    --
## measure.success = Triodia wiseana establishment        0.00     --    --
## measure.success = Agropyron desertorum seedlings       0.00     --    --
## measure.success = heterogeneous patterns of soil  ...  0.00     --    --
## measure.success = natural undisturbed communities      0.00     --    --
## measure.success = desert steppe                        0.00     --    --
## measure.success = grassland vegetation and soil        0.00     --    --
## measure.success = lizard community                     0.00     --    --
## measure.success = seed availability for restoration    0.00     --    --
## measure.success = improved soil physicochemical a ...  0.00     --    --
## measure.success = diverse arthropod community          0.00     --    --
## measure.success = grassland restoration               17.09 0.0302 94.1%
## measure.success = vegetation regeneration              0.00     --    --
## measure.success = Narduus grasslands                   0.00     --    --
## measure.success = decreasing soil P-concentration      0.00     --    --
## measure.success = soil carbon and nitrogen of the ...  0.00     --    --
## measure.success = remnant prairie vegetation           0.00     --    --
## measure.success = native vegetation                   37.84 0.0238 97.4%
## measure.success = native shrubs establishment          0.00     --    --
## measure.success = revegetation                        23.19 0.0046 95.7%
## measure.success = native perennial grasses             0.00     --    --
## measure.success = endemic sand grassland               0.00     --    --
## measure.success = native cerrado community             0.00     --    --
## measure.success = soil properties of native forests    0.00     --    --
## measure.success = not clear                            0.00     --    --
## 
## Test for subgroup differences (fixed effect model):
##                     Q d.f.  p-value
## Between groups 969.05   24 < 0.0001
## Within groups   78.11    3 < 0.0001
## 
## Results for subgroups (random effects model):
##                                                         k        
## measure.success = seedling establishment                1  0.1004
## measure.success = Triodia wiseana establishment         1 -0.1868
## measure.success = Agropyron desertorum seedlings        1  0.1973
## measure.success = heterogeneous patterns of soil  ...   1  0.1901
## measure.success = natural undisturbed communities       1 -0.0083
## measure.success = desert steppe                         1 -0.2808
## measure.success = grassland vegetation and soil         1 -0.2971
## measure.success = lizard community                      1 -0.0326
## measure.success = seed availability for restoration     1  0.3318
## measure.success = improved soil physicochemical a ...   1  0.3908
## measure.success = diverse arthropod community           1  0.1280
## measure.success = grassland restoration                 2 -0.0461
## measure.success = vegetation regeneration               1  0.5020
## measure.success = Narduus grasslands                    1  0.0613
## measure.success = decreasing soil P-concentration       1 -0.0138
## measure.success = soil carbon and nitrogen of the ...   1 -0.1297
## measure.success = remnant prairie vegetation            1 -0.2280
## measure.success = native vegetation                     2  0.1969
## measure.success = native shrubs establishment           1  0.4675
## measure.success = revegetation                          2  0.0250
## measure.success = native perennial grasses              1 -0.2642
## measure.success = endemic sand grassland                1 -0.3286
## measure.success = native cerrado community              1  0.2968
## measure.success = soil properties of native forests     1 -0.3379
## measure.success = not clear                             1  0.0430
##                                                                   95%-CI
## measure.success = seedling establishment              [-0.0416;  0.2424]
## measure.success = Triodia wiseana establishment       [-0.2919; -0.0817]
## measure.success = Agropyron desertorum seedlings      [-0.1756;  0.5702]
## measure.success = heterogeneous patterns of soil  ... [ 0.1523;  0.2280]
## measure.success = natural undisturbed communities     [-0.0913;  0.0747]
## measure.success = desert steppe                       [-0.3514; -0.2102]
## measure.success = grassland vegetation and soil       [-0.3868; -0.2073]
## measure.success = lizard community                    [-0.1910;  0.1257]
## measure.success = seed availability for restoration   [ 0.1006;  0.5630]
## measure.success = improved soil physicochemical a ... [ 0.3293;  0.4522]
## measure.success = diverse arthropod community         [-0.0646;  0.3205]
## measure.success = grassland restoration               [-0.2943;  0.2021]
## measure.success = vegetation regeneration             [ 0.3552;  0.6489]
## measure.success = Narduus grasslands                  [-0.8629;  0.9854]
## measure.success = decreasing soil P-concentration     [-0.0406;  0.0131]
## measure.success = soil carbon and nitrogen of the ... [-0.1597; -0.0997]
## measure.success = remnant prairie vegetation          [-0.3094; -0.1466]
## measure.success = native vegetation                   [-0.0196;  0.4135]
## measure.success = native shrubs establishment         [ 0.2613;  0.6737]
## measure.success = revegetation                        [-0.0708;  0.1207]
## measure.success = native perennial grasses            [-0.4402; -0.0882]
## measure.success = endemic sand grassland              [-0.4648; -0.1924]
## measure.success = native cerrado community            [-0.1971;  0.7907]
## measure.success = soil properties of native forests   [-0.3809; -0.2950]
## measure.success = not clear                           [-0.0941;  0.1801]
##                                                           Q  tau^2   I^2
## measure.success = seedling establishment               0.00     --    --
## measure.success = Triodia wiseana establishment        0.00     --    --
## measure.success = Agropyron desertorum seedlings       0.00     --    --
## measure.success = heterogeneous patterns of soil  ...  0.00     --    --
## measure.success = natural undisturbed communities      0.00     --    --
## measure.success = desert steppe                        0.00     --    --
## measure.success = grassland vegetation and soil        0.00     --    --
## measure.success = lizard community                     0.00     --    --
## measure.success = seed availability for restoration    0.00     --    --
## measure.success = improved soil physicochemical a ...  0.00     --    --
## measure.success = diverse arthropod community          0.00     --    --
## measure.success = grassland restoration               17.09 0.0302 94.1%
## measure.success = vegetation regeneration              0.00     --    --
## measure.success = Narduus grasslands                   0.00     --    --
## measure.success = decreasing soil P-concentration      0.00     --    --
## measure.success = soil carbon and nitrogen of the ...  0.00     --    --
## measure.success = remnant prairie vegetation           0.00     --    --
## measure.success = native vegetation                   37.84 0.0238 97.4%
## measure.success = native shrubs establishment          0.00     --    --
## measure.success = revegetation                        23.19 0.0046 95.7%
## measure.success = native perennial grasses             0.00     --    --
## measure.success = endemic sand grassland               0.00     --    --
## measure.success = native cerrado community             0.00     --    --
## measure.success = soil properties of native forests    0.00     --    --
## measure.success = not clear                            0.00     --    --
## 
## Test for subgroup differences (random effects model):
##                       Q d.f.  p-value
## Between groups   784.78   24 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2

forest(m, sortvar = measure.success)

radial(m)

#metabias(m, method = "linreg")

#check data coding for passive intervention and outcome vectors too - not just active?
#explore above with intervention and outcome vectors
#do with ID as level not study.ID
#lrr

#rii anew with all data?
m <- metagen(rii, var.es, studlab = ID, byvar = paradigm, data = data)
summary(m)

## Number of studies combined: k = 1329
## 
##                                          95%-CI            z p-value
## Fixed effect model   -0.0028 [-0.0028; -0.0028] -18078410.47       0
## Random effects model  0.0066 [-0.0105;  0.0237]         0.76  0.4490
## 
## Quantifying heterogeneity:
## tau^2 = 0.0351; H = 6714263.23 [6714262.64; 6714263.83]; I^2 = 100.0% [100.0%; 100.0%]
## 
## Test of heterogeneity:
##                     Q d.f. p-value
##  59868007254096752.00 1328       0
## 
## Results for subgroups (fixed effect model):
##                       k                     95%-CI                    Q
## paradigm = active  1042 -0.0028 [-0.0028; -0.0028] 59812653472614792.00
## paradigm = passive  287 -0.0186 [-0.0186; -0.0186]    55191414297898.93
##                     tau^2    I^2
## paradigm = active  0.0351 100.0%
## paradigm = passive 0.0934 100.0%
## 
## Test for subgroup differences (fixed effect model):
##                                   Q d.f. p-value
## Between groups      162367184064.81    1       0
## Within groups  59867844886912688.00 1327       0
## 
## Results for subgroups (random effects model):
##                       k                     95%-CI                    Q
## paradigm = active  1042  0.0640 [ 0.0442;  0.0838] 59812653472614792.00
## paradigm = passive  287 -0.1734 [-0.2269; -0.1199]    55191414297898.93
##                     tau^2    I^2
## paradigm = active  0.0351 100.0%
## paradigm = passive 0.0934 100.0%
## 
## Test for subgroup differences (random effects model):
##                      Q d.f.  p-value
## Between groups   66.56    1 < 0.0001
## 
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2

#forest(m, sortvar = paradigm)
#radial(m)
#metabias(m, method = "linreg")

#m <- metagen(rii, var.es, studlab = study.ID, byvar = intervention, data = active)
#summary(m)
#forest(m, sortvar = paradigm)

#m <- metagen(rii, var.es, studlab = ID, byvar = outcome, data = na.exclude(data))
#summary(m)

#however repeat above for active only - for intervention and outcome - makes more sense

Step 5b. Models

Meta and conventional statistical models to explore relative efficacy.

#effect sizes plots
ggplot(main.data, aes(paradigm, mean.rii, color = intervention)) +
  geom_point(position = position_dodge(width = 0.5)) + 
  ylim(c(-1,1)) +
  labs(x = "", y = "mean rii", color = "") +
  coord_flip() +
  geom_errorbar(aes(ymin=mean.rii-mean.var, ymax=mean.rii+mean.var), width=.05, position = position_dodge(width = 0.5)) +
  geom_hline(yintercept = 0, colour="grey", linetype = "longdash")

ggplot(simple.data, aes(paradigm, mean.rii, color = technique)) +
  geom_point(position = position_dodge(width = 0.5)) + 
  ylim(c(-1,1)) +
  labs(x = "", y = "mean rii", color = "") +
  coord_flip() +
  geom_errorbar(aes(ymin=mean.rii-error, ymax=mean.rii+error), width=.05, position = position_dodge(width = 0.5)) +
  geom_hline(yintercept = 0, colour="grey", linetype = "longdash")

ggplot(main.data, aes(paradigm, mean.rii, color = outcome)) +
  geom_point(position = position_dodge(width = 0.5)) + 
  ylim(c(-1,1)) +
  labs(x = "", y = "mean rii") +
  coord_flip() +
  geom_errorbar(aes(ymin=mean.rii-mean.var, ymax=mean.rii+mean.var), width=.05, position = position_dodge(width = 0.5)) +
  geom_hline(yintercept = 0, colour="grey", linetype = "longdash")

#ggplot(simple.data, aes(paradigm, mean.rii, color = measure.success)) +
  #geom_point(position = position_dodge(width = 0.5)) + 
  #ylim(c(-1,1)) +
  #labs(x = "", y = "mean rii") +
  #coord_flip() +
  #geom_errorbar(aes(ymin=mean.rii-error, ymax=mean.rii+error), width=.05, position = position_dodge(width = 0.5)) +
  #geom_hline(yintercept = 0, colour="grey", linetype = "longdash")

ggplot(active, aes(intervention, rii, color = outcome)) +
  geom_point(position = position_dodge(width = 0.5)) + 
  ylim(c(-1,1)) +
  labs(x = "", y = "rii") +
  coord_flip() +
  geom_errorbar(aes(ymin=rii-var.es, ymax=rii+var.es), width=.05, position = position_dodge(width = 0.5)) +
  geom_hline(yintercept = 0, colour="grey", linetype = "longdash")

ggplot(active, aes(intervention, rii, color = outcome)) +
  geom_boxplot() + 
  ylim(c(-1,1)) +
  labs(x = "", y = "rii") +
  coord_flip() +
  geom_errorbar(aes(ymin=rii-var.es, ymax=rii+var.es), width=.05, position = position_dodge(width = 0.5)) +
  geom_hline(yintercept = 0, colour="grey", linetype = "longdash")

#simple.active <- active %>%
  #group_by(study.ID, outcome) %>%
  #summarise(n = n(), mean.rii = mean(rii), mean.var = #mean(var.es))

#t-tests if different from 0
#t-tests if mean rii different from mu = 0
tmu <- function(x)
{t.test(x, mu = 0, paired = FALSE, var.equal=FALSE, conf.level = 0.95)
}

data %>%
  split(.$paradigm) %>%
  purrr::map(~tmu(.$rii))

## $active
## 
##  One Sample t-test
## 
## data:  x
## t = 7.9137, df = 1630, p-value = 4.577e-15
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.06472721 0.10738564
## sample estimates:
##  mean of x 
## 0.08605643 
## 
## 
## $passive
## 
##  One Sample t-test
## 
## data:  x
## t = -5.0636, df = 372, p-value = 6.488e-07
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.13662799 -0.06019472
## sample estimates:
##   mean of x 
## -0.09841136

active %>%
  split(.$intervention) %>%
  purrr::map(~tmu(.$rii))

## $`carbon amendment`
## 
##  One Sample t-test
## 
## data:  x
## t = -5.2225, df = 71, p-value = 1.686e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.3492415 -0.1562481
## sample estimates:
##  mean of x 
## -0.2527448 
## 
## 
## $`mycorrhizal inoculation`
## 
##  One Sample t-test
## 
## data:  x
## t = 13.364, df = 151, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.2615316 0.3522823
## sample estimates:
## mean of x 
##  0.306907 
## 
## 
## $nutrients
## 
##  One Sample t-test
## 
## data:  x
## t = 8.7479, df = 129, p-value = 1.019e-14
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.2807864 0.4449217
## sample estimates:
## mean of x 
##  0.362854 
## 
## 
## $planting
## 
##  One Sample t-test
## 
## data:  x
## t = 5.125, df = 864, p-value = 3.672e-07
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.04527295 0.10147166
## sample estimates:
##  mean of x 
## 0.07337231 
## 
## 
## $seeding
## 
##  One Sample t-test
## 
## data:  x
## t = -0.03268, df = 388, p-value = 0.9739
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.04497171  0.04350115
## sample estimates:
##     mean of x 
## -0.0007352816 
## 
## 
## $vegetation
## 
##  One Sample t-test
## 
## data:  x
## t = 0.0091906, df = 10, p-value = 0.9928
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.3642764  0.3672939
## sample estimates:
##   mean of x 
## 0.001508783 
## 
## 
## $`water addition`
## 
##  One Sample t-test
## 
## data:  x
## t = 5.0865, df = 11, p-value = 0.0003514
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.07266727 0.18352542
## sample estimates:
## mean of x 
## 0.1280963

active %>%
  split(.$outcome) %>%
  purrr::map(~tmu(.$rii))

## $`invertebrate community`
## 
##  One Sample t-test
## 
## data:  x
## t = 1.3026, df = 23, p-value = 0.2056
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.07526574  0.33120545
## sample estimates:
## mean of x 
## 0.1279699 
## 
## 
## $`native species`
## 
##  One Sample t-test
## 
## data:  x
## t = 10.495, df = 468, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.1928828 0.2817528
## sample estimates:
## mean of x 
## 0.2373178 
## 
## 
## $`soil quality`
## 
##  One Sample t-test
## 
## data:  x
## t = 9.6158, df = 421, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.1594986 0.2414609
## sample estimates:
## mean of x 
## 0.2004797 
## 
## 
## $unreported
## 
##  One Sample t-test
## 
## data:  x
## t = -7.3944, df = 13, p-value = 5.237e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.3782394 -0.2071959
## sample estimates:
##  mean of x 
## -0.2927177 
## 
## 
## $`vegetation and habitat`
## 
##  One Sample t-test
## 
## data:  x
## t = -5.9951, df = 694, p-value = 3.269e-09
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.10369991 -0.05253375
## sample estimates:
##   mean of x 
## -0.07811683 
## 
## 
## $`vertebrate community`
## 
##  One Sample t-test
## 
## data:  x
## t = -0.40398, df = 6, p-value = 0.7002
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.2303505  0.1650673
## sample estimates:
##   mean of x 
## -0.03264162

#meta-stats for active versus passive

#meta-stats for differences between interventions and outcomes for active test studies